Controllable radio interference for experimental and testing purposes in wireless sensor networks

Abstract—We address the problem of generating customized, controlled interference for experimental and testing purposes in Wireless Sensor Networks. The known coexistence problems between electronic devices sharing the same ISM radio band drive the design of new solutions to minimize interference. The validation of these techniques and the assessment of protocols under external interference require the creation of reproducible and well-controlled interference patterns on real nodes, a nontrivial and time-consuming task. In this paper, we study methods to generate a precisely adjustable level of interference on a specific channel, with lowcost equipment and rapid calibration. We focus our work on the platforms carrying the CC2420 radio chip and we show that, by setting such transceiver in special mode, we can quickly and easily generate repeatable and precise patterns of interference. We show how this tool can be extremely useful for researchers to quickly investigate the behaviour of sensor network protocols and applications under different patterns of interference, and we further evaluate its performance

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Information Centric Strategies for Scalable Data Transport in Cyber Physical Systems (CPSs)

Cyber-Physical Systems (CPSs) represent the next generation of computing that is ubiquitous, wireless and intelligent. These networked sens- ing systems are at the intersection of sensing, communication, control, and computing [16]. Such systems will have applications in numerous elds such as vehicular systems and transportation, medical and health care systems, smart homes and buildings, etc. The proliferation of such sensing systems will trigger an exponential increase in the computational devices that exchange data over existing network infrastructure.;Transporting data at scale in such systems is a challenge [21] mainly due to the underlying network infrastructure which is still resource con- strained and bandwidth-limited. Eorts have been made to improve the network infrastructure [5] [2] [15]. The focus of this thesis is to put forward information-centric strategies that optimize the data transport over existing network infrastructure.;This thesis proposes four dierent information-centric strategies: (1) Strategy to minimize network congestion in a generic sensing system by estimating data with adaptive updates, (2) An adaptive information exchange strategy based on rate of change of state for static and mobile networks, (3) Spatio-temporal strategy that maintains spatial resolution by reducing redundant transmissions, (4) Proximity-dependent data transfer strategy to ensure most updated information in high-density regions. Each of these strategies is experimentally veried to optimize the data transport in their respective setting

Correlation-based Cross-layer Communication in Wireless Sensor Networks

Wireless sensor networks (WSN) are event based systems that rely on the collective effort of densely deployed sensor nodes continuously observing a physical phenomenon. The spatio-temporal correlation between the sensor observations and the cross-layer design advantages are significant and unique to the design of WSN. Due to the high density in the network topology, sensor observations are highly correlated in the space domain. Furthermore, the nature of the energy-radiating physical phenomenon constitutes the temporal correlation between each consecutive observation of a sensor node. This unique characteristic of WSN can be exploited through a cross-layer design of communication functionalities to improve energy efficiency of the network. In this thesis, several key elements are investigated to capture and exploit the correlation in the WSN for the realization of advanced efficient communication protocols. A theoretical framework is developed to capture the spatial and temporal correlations in WSN and to enable the development of efficient communication protocols. Based on this framework, spatial Correlation-based Collaborative Medium Access Control (CC-MAC) protocol is described, which exploits the spatial correlation in the WSN in order to achieve efficient medium access. Furthermore, the cross-layer module (XLM), which melts common protocol layer functionalities into a cross-layer module for resource-constrained sensor nodes, is developed. The cross-layer analysis of error control in WSN is then presented to enable a comprehensive comparison of error control schemes for WSN. Finally, the cross-layer packet size optimization framework is described.Ph.D.Committee Chair: Ian F. Akyildiz; Committee Member: Douglas M. Blough; Committee Member: Mostafa Ammar; Committee Member: Raghupathy Sivakumar; Committee Member: Ye (Geoffrey) L

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management